Excellent — DICTIONARY MASTERY – DAY 3 is the capstone.
Today you’ll connect hash tables → real system design and finish with interview-level speed drills.

🔥 DICTIONARY MASTERY — DAY 3

LRU Cache, Ordered Dicts, Advanced Design Patterns & Rapid Interview Drills

🧠 DAY 3 GOAL

By the end of today, you will:

• Implement LRU cache from scratch
• Understand dict order semantics deeply
• Choose correctly between dict, defaultdict, Counter
• Solve design + coding hybrid questions
• Answer senior-level dict interview questions confidently

1️⃣ LRU CACHE — THE MOST IMPORTANT DICT DESIGN QUESTION

Problem

Design a cache with:

• get(key) → O(1)
• put(key, value) → O(1)
• Evict Least Recently Used item

🧠 Core Insight

You need:

• Fast lookup → dict
• Fast order update → doubly linked list

Dict alone is not enough.

🔥 LRU IMPLEMENTATION (INTERVIEW-GRADE)

class Node:
    def __init__(self, key, val):
        self.key = key
        self.val = val
        self.prev = None
        self.next = None


class LRUCache:
    def __init__(self, capacity):
        self.cap = capacity
        self.map = {}

        # Dummy head & tail
        self.head = Node(0, 0)
        self.tail = Node(0, 0)
        self.head.next = self.tail
        self.tail.prev = self.head

    def _remove(self, node):
        p, n = node.prev, node.next
        p.next, n.prev = n, p

    def _add(self, node):
        # add to front (most recent)
        node.next = self.head.next
        node.prev = self.head
        self.head.next.prev = node
        self.head.next = node

    def get(self, key):
        if key not in self.map:
            return -1
        node = self.map[key]
        self._remove(node)
        self._add(node)
        return node.val

    def put(self, key, value):
        if key in self.map:
            self._remove(self.map[key])
        node = Node(key, value)
        self.map[key] = node
        self._add(node)

        if len(self.map) > self.cap:
            lru = self.tail.prev
            self._remove(lru)
            del self.map[lru.key]

🧠 Interview explanation:

“Dict gives O(1) access, doubly linked list maintains usage order.”

⚡ Python Shortcut (Mention in Interview)

from collections import OrderedDict

class LRUCache:
    def __init__(self, cap):
        self.cap = cap
        self.cache = OrderedDict()

    def get(self, k):
        if k not in self.cache:
            return -1
        self.cache.move_to_end(k)
        return self.cache[k]

    def put(self, k, v):
        if k in self.cache:
            self.cache.move_to_end(k)
        self.cache[k] = v
        if len(self.cache) > self.cap:
            self.cache.popitem(last=False)

Interview tip:

• Mention both
• Implement manual version if asked

2️⃣ dict vs defaultdict vs Counter (VERY IMPORTANT)

dict

Use when:

• Full control needed
• Simple key-value storage

defaultdict

from collections import defaultdict

Use when:

• Grouping
• Frequency counting
• Avoiding key checks

Trap:

• Default created on access
• Can grow unintentionally

Counter

from collections import Counter

Use when:

• Frequency problems
• Top-k elements
• Comparing counts

But:

• Less flexible
• Slight overhead vs dict

🧠 Interview rule:

“Use Counter when intent is counting.”

3️⃣ ORDERED DICTS — WHAT REALLY MATTERS NOW

Since Python 3.7:

• dict preserves insertion order
• OrderedDict mostly unnecessary

Still useful for:

• move_to_end
• Explicit order manipulation
• Older Python versions

Interview line:

“I’d use dict unless I need explicit order operations.”

4️⃣ DICT AS STATE MACHINE (ADVANCED THINKING)

Dicts track state transitions:

Examples:

• Memoization
• Caching
• Visited nodes
• Sliding window state

memo = {}
def fib(n):
    if n in memo:
        return memo[n]
    memo[n] = fib(n-1) + fib(n-2)
    return memo[n]

Mentioning “state compression” = senior signal.

5️⃣ REAL-WORLD DESIGN QUESTIONS (DICT-HEAVY)

🔥 Design 1: API Rate Limiter

• Key: user_id
• Value: timestamps / counters
• Dict + sliding window

🔥 Design 2: Log Aggregator

• Key: log level
• Value: list/count

🔥 Design 3: Cache with TTL

• Dict value = (data, expiry)
• Lazy eviction

Explaining these verbally is interview gold.

🔥 COMMON DICT DESIGN TRAPS 🚨

❌ Using list for lookup
❌ Using mutable keys
❌ Forgetting eviction policy
❌ Ignoring memory growth
❌ Overusing setdefault

🔥 30 RAPID-FIRE DICT INTERVIEW QUESTIONS

Answer confidently:

1. Why dict lookup is O(1)?
2. Worst-case dict complexity?
3. Why keys must be hashable?
4. Why dict preserves order?
5. get() vs []?
6. defaultdict trap?
7. Counter use cases?
8. When OrderedDict needed?
9. How LRU works?
10. Why linked list needed?
11. How to evict efficiently?
12. Prefix hash intuition?
13. Why tuple as key?
14. Can dict store functions?
15. Why hashing cached?
16. How collisions resolved?
17. Why resize expensive?
18. How Python avoids attacks?
19. Dict vs set internals?
20. Memory overhead of dict?
21. Dict iteration order?
22. Removing during iteration?
23. Copy cost?
24. Deep vs shallow?
25. When dict not suitable?
26. Dict vs database?
27. Dict thread-safety?
28. Hash randomization?
29. Dict equality cost?
30. Best dict interview pattern?

If you can explain these → you pass senior Python interviews.

🧠 FINAL DICT MENTAL MODEL

Think:

1. Hashability
2. O(1) expectation
3. Order matters?
4. Growth & eviction
5. State vs storage

📝 FINAL DICT ASSIGNMENT (MANDATORY)

1️⃣ Predict output (NO RUNNING)

from collections import OrderedDict

d = OrderedDict()
d["a"] = 1
d["b"] = 2
d["a"] = 3
print(list(d.keys()))

2️⃣ Coding (INTERVIEW CLASSIC)

def top_k_frequent(nums, k):
    """
    Use dict-based approach
    """

3️⃣ Explain (Interview style)

Why LRU cache cannot be implemented with dict alone (without order structure).

🎯 YOU HAVE COMPLETED DICTIONARY MASTERY

You now have:
✔ Hash table internals
✔ Method-level mastery
✔ Core & advanced patterns
✔ System-design readiness

🔜 NEXT MODULE OPTIONS (Choose One)

👉 “START SET & TUPLE MASTERY (Module 4)”

(hashability, math ops, interview patterns)

👉 “START PYTHON CODING INTERVIEW BLITZ”

(mixed problems, timed, FAANG-style)

👉 “START REAL-WORLD PYTHON PROJECTS TRACK”

(build systems using lists + dicts + strings)

Say your choice — we continue immediately 🚀